Biology subjects

Locard-Paulet, M.

Publications and source records attributed to Locard-Paulet, M..

7 recordsLinked to original sources

bioRxiv2024

Biased recruitment of H3.3 by HIRA is dictated by de-/acetylation and determines transcription memory and response

Chromatin plasticity and epigenetic memory, fundamental for eukaryotic biology, are determined by differential/regulated de novo deposition or recycling of pre-existing histones, which in turn dictate transcriptional programs. Recruitment of the histone-H3 variant, H3.3, mediated by the HIRA chaperone complex, is both causally and consequentially associated with transcription. Despite decades of work, endogenous regulatory mechanisms that differentiate between de novo deposition and recycling activities of HIRA are still unknown. Here, we have investigated the pivotal role of HIRA de-/acetylation in regulating its function. Our results unequivocally establish function separation effects of acetyl and deacetyl mimic mutations of lysine-600, vis-a-vis de novo deposition or recycling of H3.3, respectively. Importantly, we demonstrate that HIRA deacetylation-dependent biased H3.3 recycling determines transcriptional output, possibly through preferential enrichment of H3.3-K36me3. Besides unraveling tunable regulatory mechanism that governs HIRA function, we illustrate a causal link between the chaperone activity, biased recruitment of pre-existing histones, and gene expression.

molecular biology↗

bioRxiv2024

STREAMLINED PROTEOME-WIDE IDENTIFICATION OF DRUG TARGETS INDICATES ORGAN-SPECIFIC ENGAGEMENT

Proteins are the primary targets of almost all small molecule drugs. However, even the most selectively designed drugs can potentially target several unknown proteins. Identification of potential drug targets can facilitate design of new drugs and repurposing of existing ones. Current state-of-the-art proteomics methodologies enable screening of thousands of proteins against a limited number of drug molecules. Here we report the development of a label-free quantitative proteomics approach that enables proteome-wide screening of small organic molecules in a scalable, reproducible, and rapid manner by streamlining the proteome integral solubility alteration (PISA) assay. We used rat organs ex-vivo to determine organ specific targets of medical drugs and enzyme inhibitors to identify novel drug targets for common drugs such as Ibuprofen. Finally, global drug profiling revealed overarching trends of how small molecules affect the proteome through either direct or indirect protein interactions.

biochemistry↗

bioRxiv2023

A site-specific analysis of the ADP-Ribosylome unveils Homogeneous DNA Damage-Induced Serine ADP-Ribosylation across wild-type and BRCA-mutant Breast Cancer cell lines

ADP-ribosylation (ADPr) signaling plays a crucial role in the DNA damage response. Inhibitors against the main enzyme catalyzing ADPr after DNA damage - PARP1 - are used as targeted therapies against breast cancers with BRCA1/2 mutations. However, development of resistance to PARP inhibitors (PARPi) is a major obstacle in treating patients. To better understand the role of ADPr in PARPi sensitivity, we used Liquid Chromatography-Mass Spectrometry (LC-MS) for systems level analysis of the ADP-ribosylome in six breast cancer cell lines exhibiting different PARPi sensitivities. We identified 1,632 sites on 777 proteins across all cell lines, primarily on serine residues, with site-specific overlap of targeted residues across DNA damage-related proteins across all cell lines, demonstrating high conservation of serine ADPr signaling networks upon DNA damage. We furthermore observed site-specific differences in ADPr intensities in PARPi-sensitive BRCA mutants, and unique ADPr sites in PARPi-resistant BRCA mutant cells, which we notably show to have low PARG levels and longer ADPr chains on PARP1.

cell biology↗

bioRxiv2023

Mass spectrometry-based proteomics imputation using self supervised deep learning

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

bioinformatics↗

bioRxiv2022

Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies

Today, hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Genes can also be differentially regulated because of confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. Here, we identified genes for which expression consistently correlates with cell proliferation rates in proteomics and transcriptomics high-throughput data sets to determine the overall impact of cell growth rate on these data. We combined the analysis of 449 cell lines and 1,040 cell lines in five proteomics and three transcriptomics data sets to generate a refined list of 223 confounding genes that correlate with cell proliferation rates. These include many actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. It constitutes a valuable resource when analyzing high-throughput datasets showing changes in proliferation across conditions. We show how to use this resource to analyze in vitro drug screens and tumor samples. By disregarding the proliferation confounders, one can instead focus on the experiment-specific regulation events otherwise buried in the statistical analysis.

cell biology↗

bioRxiv2021

A proteomics sample metadata representation for multiomics integration, and big data analysis.

The amount of public proteomics data is increasing at an extraordinary rate. Hundreds of datasets are submitted each month to ProteomeXchange repositories, representing many types of proteomics studies, focusing on different aspects such as quantitative experiments, post-translational modifications, protein-protein interactions, or subcellular localization, among many others. For every proteomics dataset, two levels of data are captured: the dataset description, and the data files (encoded in different file formats). Whereas the dataset description and data file formats are supported by all ProteomeXchange partner repositories, there is no standardized format to properly describe the sample metadata and their relationship with the dataset files in a way that fully allows their understanding or re-analysis. It is left to the users choice whether to provide or not an ad hoc document containing this information. Therefore, in many cases, understanding the study design and data requires going back to the associated publication. This can be tedious and may be restricted in the case of non-open access publications. In many cases, this problem limits the generalization and reuse of public proteomics data. Here we present a standard representation for sample metadata tailored to proteomics datasets produced by the HUPO Proteomics Standards Initiative and supported by ProteomeXchange resources. We repurposed the existing data format MAGE-TAB used routinely in the transcriptomics field to represent and annotate proteomics datasets. MAGETAB-Proteomics defines a set of annotation rules that the datasets submitted to ProteomeXchange should follow, ranging from sample properties to data analysis protocols. We also introduce a crowdsourcing project that enabled the manual curation of over 200 public datasets using MAGE-TAB-Proteomics. In addition, we describe an ecosystem of tools and libraries that were developed to validate and submit sample metadata-related information to ProteomeXchange. We expect that these tools will improve the reproducibility of published results and facilitate the reanalysis and integration of public proteomics datasets.

bioinformatics↗

bioRxiv2020

Conformational maps of human 20S proteasomes reveal PA28- and immuno-dependant inter-ring crosstalks

Here, we used for the first time Hydrogen-Deuterium eXchange coupled to Mass Spectrometry (HDX-MS) to investigate conformational differences between the human standard 20S (std20S) and immuno 20S (i20s) proteasomes alone or in complex with PA28{beta} or PA28{gamma} activators. Their solvent accessibility was analyzed through a dedicated bioinformatic pipeline including stringent statistical analysis and 3D visualization. These data confirmed the existence of allosteric differences between the std20S and i20S at the surface of the -ring triggered from inside the catalytic {beta}-ring. Additionally, binding of the PA28 regulators to the 20S proteasomes modified solvent accessibility due to conformational changes of the {beta}-rings. This work is not only a proof-of-concept that HDX-MS can be used to get structural insights on large multi-protein complexes in solution, it also demonstrates that the binding of the std20S or i20S subtype to any of its PA28 activator triggers allosteric changes that are specific to this 20S/PA28 pair.

biophysics↗